Quantile regression with censored data using generalized L1minimization

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Sammanfattning

We propose a way to estimate a parametric quantile function when the dependent variable, e.g. the survival time, is censored. We discuss one way to do this, transforming the problem of finding the p-quantile for the true, uncensored, survival times into a problem of finding the q-quantile for the observed, censored, times. The q-value involves the distribution of the censoring times, which is unknown. The estimation of the quantile function is done using the asymmetric L1 technique with weights involving local Kaplan-Meier estimates of the distribution of the censoring limit.
Originalspråkengelska
Sidor (från-till)509-524
TidskriftComputational Statistics & Data Analysis
Volym23
Utgåva4
DOI
StatusPublished - 1997

Ämnesklassifikation (UKÄ)

  • Sannolikhetsteori och statistik

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